A novel green antenna phase-shift system with data acquisition boards

A novel green phase shifter system is proposed in this research. The system is developed by a combination of reconfigurable beam steering antennas and data acquisition (DAQ) boards. A combination of two reconfigurable beam steering antennas, located side-by-side, forms a spatial configuration structure with a fabricated ‘green’ element plank of rice husk placed in between. The concept of a spatial configuration technique has been ‘mutated’ by shifting the structure of spiral feed line and aperture slots of first beam steering antenna by as much as 45 ◦ . The PIN diode switches connected to the DAQ boards enable the intelligent capability of the spatial antennas. The activation of certain degree radiation patterns of either the first beam steering antenna or the second beam steering antenna depends on the memory of the DAQ boards — Beam Manager. When an intruder comes from the cardinal angles of 0◦/ 360◦, 90◦, 180◦, or 270◦, its range and angles’ location will be automatically detected by the first antenna through the output ports of the 1st DAQ: P1.0, P1.1, P1.2, and P1.3. The second antenna is then activated by the output ports of the 2nd DAQ: P2.0 up to P2.3, to adaptively maneuver the beam towards four different ordinal directions of 45◦, 135◦, 225◦, and 315◦

VOTERS’ PERCEPTION TOWARDS POLITICAL PARTY: A GEOSPATIAL CASE STUDY OF YOUNG VOTERS IN JOHOR BAHRU

The application of Geographic Information System (GIS) and election is a study that stresses geospatial aspects which gives support to election boundaries, election information management system along with GIS analysis in election boundaries. This article, as a concept, explains the role and function of GIS and geopolitics as an introduction to examine the election pattern in Malaysia. This study was aimed at understanding the issue that influenced youth voters in DUN Nusajaya to cast their votes in General Election, thus charting new strategies for future election. In doing so, nine issues are carefully selected and analysed from a geographical perspective by using GIS method. Socio-demographic data, issues influenced and assessment of state political figure, are used to explore geographic variability in relationships between each variable at a detailed spatial level. Spatial analysis using geographically weighted regression (GWR) was employed, which enables the investigation of local variations in voting patterns. The results demonstrate that assessment of state political figures do vary over geographic space and some of the variables that are traditionally assumed affect certain locations

Evaluation of phenolics, capsaicinoids, antioxidant properties, and major macro-micro minerals of some hot and sweet peppers and ginger land-races of Malaysia

There are compelling evidences that oxidative stress plays important role in age‐related neurodegenerative diseases and natural food‐derived dietary antioxidants appear to be the first line of defence to take care of the oxidative stress. The analysis results of this study showed that Cili Burung of the hot pepper landraces contained the highest amount of TPC (1.00 mg/g DW), TFC (0.17 mg/g DW), capsaicinoids (0.05 mg/g), and the highest FRAP activity (439 mg/g DW). However, DPPH assay revealed the highest DPPH scavenging activity (IC50 = 250 μg/ml) in Halia Bara rhizome. Rhizomes of both landraces of ginger showed a significant amount of capsaicinoids. Hot pepper Cili Ungu (5.50 g/kg) and sweet pepper Cili Kulai had the highest P contents (~5.5 g/kg). Halia Bentong ginger rhizome had the highest content of K (33.84 g/kg); however, Halia Bentong ginger roots had remarkably greater contents of Ca (10.96 g/kg). Ginger rhizomes and roots had greater contents of micro minerals compared to hot and sweet chili peppers. Ginger roots contained good amounts of bioactive compounds and mineral nutrients indicating its commercial value

Investigation on dynamic performance of concrete column crumb rubber steel and fiber concrete

In general the Normal Concrete (NC) are by quasi-brittle failure, where, the nearly complete loss of loading capacity, once failure is initiated especially under dynamic loadings. The significance of this study is to improve the damping properties of concrete structure by utilization of the recycled materials from waste tires to be used in concrete as structural materials that improve seismic performance. In this study, the concrete containing 10% of fine crumb rubber and 1 % volume fraction of steel fiber from waste tires is use to investigate the dynamic performance (natural frequency and damping ratio).A small scale column were fabricated from Treated Crumb Rubber and Steel Fiber Concrete (TCRSFC) and NC were cast and cured for 28 days to investigate the dynamic performance. Based on analysis, dynamic modulus, damping ratio and natural frequency of TCRSFC has improved considerably by 5.18%, 109% and 10.94% when compared with NC. The TCRSFC producing concrete with the desired properties as well as to introduce the huge potential as dynamic resistance structure from severe damage especially prevention on catastrophic failure

Assessment on recent landslide susceptibility mapping methods: A review

Landslide is a destructive natural hazard that causes severe property loss and loss of lives. Numerous researchers have developed landslide susceptibility maps in order to forecast its occurrence, particularly in hill-site development. Various quantitative approaches are used in landslide susceptibility map production, which can be classified into three categories; statistical data mining, machine learning and deterministic approach. In this paper, we choose two regular models in each category, which are Weight of Evidence (WoE) and Frequency Ratio (FR), Artificial Neutral Networks (ANN) and Support Vector Machines (SVM), Shallow Landsliding Stability Model (SHALSTAB) and YonSei-Slope (YS-Slope). Discussion and assessment on these models are based on relevant literature

Development of An Automated Configuration System for Robot Work Cell

Configuration robot work cell has received considerable attention in the last few years due to it is very knowledge-intensive, intricate, and time-consuming process. This paper elaborated the development process of the automated configuration system (ACS) for (re-)configuring robot work cell while satisfying certain requirements of users in an innovative way. The primary purpose of this work was to provide a fast configuration system with less cost and human involvement at little or no further investment. The ACS was constructed based on the variant-shaped configuration concept with its mathematical model. A configuration and programming structure with a graphical user interface (GUI) were the outcomes of this work that were capable of determining the optimal robot work cell according to the user requirements e.g. the number of a robot, Nr and the types of configuration. This work utilized both macro and Visual Basic (VB) editor in CATIA 3D CAD software for creating a completed user interface. The current outcomes of this work will provide a basis for future investigation in determining the optimal layout of robot work cell that is dependable on other requirements

ASSIMILATING GEOSPATIAL METAMODEL AND INVENTORY MAPPING FOR NON-STRUCTURAL MITIGATION OF LANDSLIDE

In Malaysia, issues related to disaster management are always given attention in society and by the responsible parties. However, in general, citizen do not think of the consequential impact of disaster due to less of knowledge regarding the early phase in disaster management. Therefore, citizen in those areas will be more vulnerable to landslide as the citizen face difficulties in identifying specific areas with the tendency of landslides. This paper presents a geospatial metamodel approach for non-structural mitigation of landslide using data from airborne LiDAR and aerial photograph. Disaster management metamodel with geospatial element combines activity for managing disaster along with geospatial database that makes it handy for appreciating the metamodel. On the other hand, the digital terrain model (DTM) from LiDAR and aerial photograph is required to produce landslide inventory mapping. The case study area is located in Kundasang, Sabah, where landslides occur frequently. In order to get better visual in identifying landslides in the study area, three types of data are required to carry out image interpretation. The three types of data are hillshade, topographic openness and colour composite. The result of the landslide inventory map shows that there are five types of landslide, which is debris flow, debris fall, mud flow, deep-seated landslide and shallow landslide. Finally, the result of landslide inventory map will be integrated into the developed metamodel for presentation to the users. This landslide inventory map is used as a non-structural mitigation step in one of disaster management phases that is suitable to prepare and use in mitigating the landslide hazard impact

GEOSPATIAL APPROACH FOR LANDSLIDE ACTIVITY ASSESSMENT AND MAPPING BASED ON VEGETATION ANOMALIES

Remote sensing has been widely used for landslide inventory mapping and monitoring. Landslide activity is one of the important parameters for landslide inventory and it can be strongly related to vegetation anomalies. Previous studies have shown that remotely sensed data can be used to obtain detailed vegetation characteristics at various scales and condition. However, only few studies of utilizing vegetation characteristics anomalies as a bio-indicator for landslide activity in tropical area. This study introduces a method that utilizes vegetation anomalies extracted using remote sensing data as a bio-indicator for landslide activity analysis and mapping. A high-density airborne LiDAR, aerial photo and satellite imagery were captured over the landslide prone area along Mesilau River in Kundasang, Sabah. Remote sensing data used in characterizing vegetation into several classes of height, density, types and structure in a tectonically active region along with vegetation indices. About 13 vegetation anomalies were derived from remotely sensed data. There were about 14 scenarios were modeled by focusing in 2 landslide depth, 3 main landslide types with 3 landslide activities by using statistical approach. All scenarios show that more than 65% of the landslides are captured within 70% of the probability model indicating high model efficiency. The predictive model rate curve also shows that more than 45% of the independent landslides can be predicted within 30% of the probability model. This study provides a better understanding of remote sensing data in extracting and characterizing vegetation anomalies induced by hillslope geomorphology processes in a tectonically active region in Malaysia